2 Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China, Yinchuan 750021,Ningxia,China;3Ningxia Hui Autonomous Region Desert Information Intellisense Key Laboratory,Ningxia University,Yinchuan 750021,Ningxia,China
Received date: 2019-02-10
Revised date: 2019-05-24
Online published: 2019-09-19
This paper used the object-oriented method and CART decision tree method to extract the sand information with high degree of automation and comprehensive extraction features.The main research process is as follows: (1) Select the study area and preprocess the image of the study area. (2) Use multi-scale segmentation and spectral difference segmentation to obtain the object layer. (3) Select rich extraction features and training sample objects. (4) Training features and sample objects to get the CART rule tree. (5) Apply all objects to the rule tree to get the classification result. (6) Compare the Nearest neighbor and Support vector machine classification results.Finally,Compared with the current research on extracting sand information by CART decision tree.The overall classification accuracy reached 77%,which is 1.12 times of the Nearest neighbor classification result,1.57 times of the support vector machine classification result.In addition,normalized diffevence bare index (NDBI), granularity size index (GSI) and the seventh band (SWIR 2) can successfully distinguish three easily mixed objects of sand, Gobi and bare rock,which are three important characteristic indexes in the process of sand extraction.The experiment has proven this method is a feasible sand extraction method for actual desertification monitoring.
